{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn as sk\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "from sklearn import cluster, datasets, mixture\n",
    "from sklearn.neighbors import kneighbors_graph\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from itertools import cycle, islice\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 先设置好一些参数，比如随机数的种子，样本数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kmeans 对cluster数目非常敏感"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1mf9i3C+JskdR7irns5xPyK7OIj1+GOcPu5D+0f353bTf88eld+OzfHhMD7rQ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\na+Sdz2shzgoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109992850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,8))\n",
    "\n",
    "## 大家可以尝试对上面的数据做一下Kmeans 聚类，类的数目可以自己定义\n",
    "## 比如\n",
    "n_clusters = [2, 3, 4, 5]\n",
    "for i, n in enumerate(n_clusters):\n",
    "    plt.subplot(2, 2, i + 1)\n",
    "    y_pred = cluster.KMeans(n_clusters = n, random_state=random_state).fit_predict(X)\n",
    "    colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',\n",
    "                                     '#f781bf', '#a65628', '#984ea3',\n",
    "                                     '#999999', '#e41a1c', '#dede00']),\n",
    "                              int(max(y_pred) + 1))))\n",
    "    plt.scatter(X[:, 0], X[:, 1], color = colors[y_pred])\n",
    "    plt.title(\"{} cluster\".format(n))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kmeans对数据分布的敏感度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对数据进行一个转换，使得不再均匀分布， 有各向异性\n",
    "# Kmeans的前提条件是cluster是圆形区域的\n",
    "\n",
    "\n",
    "## 对X_aniso 做Kmeans，看看有什么结论\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对数据进行一个转换，使得不再均匀分布， 有各向异性\n",
    "\n",
    "# Kmeans的前提条件是cluster是圆形区域的\n",
    "\n",
    "\n",
    "\n",
    "## 对X_aniso 做Kmeans，看看有什么结论\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 圆形分布的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# connectivity concept, kneighbors_graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义不同clustering算法的训练以及可视化函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_cluster(X, y, params):\n",
    "\n",
    "    # 利用kneighbors_graph， 建立 connectivity\n",
    "    connectivity = kneighbors_graph(X, n_neighbors = params[\"n_neighbors\"])\n",
    "    # make connectivity symmetric\n",
    "    connectivity = 0.5 * (connectivity + connectivity.T)\n",
    "    \n",
    "    # 定义kmeans，cluster数目根据传进来的参量\n",
    "    kmeans = cluster.KMeans(n_clusters = params[\"n_clusters\"])\n",
    "\n",
    "    # 定义DBSCAN，eps和min_samples由传进来的params参量获得\n",
    "    dbscan = cluster.DBSCAN(eps=params['eps'], min_samples = params[\"min_samples\"])\n",
    "\n",
    "    # 根据eulidean距离的average还是maximum得到 AgglomerativeClustering算法的两种average和complete linkage\n",
    "    average_linkage = cluster.AgglomerativeClustering(linkage = \"average\",\n",
    "                                                      affinity = \"euclidean\",\n",
    "                                                      n_clusters = params['n_clusters'],\n",
    "                                                      connectivity = connectivity)\n",
    "\n",
    "    complete_linkage = cluster.AgglomerativeClustering(linkage = \"complete\",\n",
    "                                                       affinity = \"euclidean\",\n",
    "                                                       n_clusters = params['n_clusters'],\n",
    "                                                       connectivity = connectivity)\n",
    "    # GMM算法\n",
    "    gmm = mixture.GaussianMixture(n_components = params['n_clusters'])\n",
    "\n",
    "    # Spectral clustering的算法\n",
    "    spectral = cluster.SpectralClustering(n_clusters = params['n_clusters'], \n",
    "                                          affinity = \"nearest_neighbors\")\n",
    "\n",
    "    clustering_algorithms = (\n",
    "        ('Kmeans', kmeans),\n",
    "        ('DBSCAN', dbscan),\n",
    "        ('Average linkage agglomerative clustering', average_linkage),\n",
    "        ('Complete linkage agglomerative clustering', complete_linkage),\n",
    "        ('Spectral clustering', spectral),\n",
    "        ('GaussianMixture', gmm)\n",
    "    )\n",
    "\n",
    "\n",
    "    plt.figure(figsize = (10, 10))\n",
    "\n",
    "    for i, (alg_name, algorithm) in enumerate(clustering_algorithms):\n",
    "            t0 = time.time()\n",
    "            with warnings.catch_warnings():\n",
    "                warnings.simplefilter(\"ignore\")\n",
    "                algorithm.fit(X)\n",
    "            t1 = time.time()\n",
    "            if hasattr(algorithm, 'labels_'):\n",
    "                y_pred = algorithm.labels_.astype(np.int)\n",
    "            else:\n",
    "                y_pred = algorithm.predict(X)  # GMM\n",
    "\n",
    "            plt.subplot(3, 2, i + 1)        \n",
    "            colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',\n",
    "                                                 '#f781bf', '#a65628', '#984ea3',\n",
    "                                                 '#999999', '#e41a1c', '#dede00']),\n",
    "                                          int(max(y_pred) + 1))))\n",
    "            plt.scatter(X[:, 0], X[:, 1], s = 10, color=colors[y_pred])\n",
    "\n",
    "            plt.xlim(-2.5, 2.5)\n",
    "            plt.ylim(-2.5, 2.5)\n",
    "            plt.xticks(())\n",
    "            plt.yticks(())\n",
    "            plt.title(alg_name)\n",
    "            plt.text(.99, .01, ('time = %.2fs' % (t1 - t0)).lstrip('0'),\n",
    "                     transform=plt.gca().transAxes, size=15,\n",
    "                     horizontalalignment='right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## moon shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datasets.make_blobs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## blobs with varied variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## anisotropy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 层次聚类 dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.cluster import hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hierarchy.linkage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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